用面部表情视频提升疼痛分类准确率，适合无法言语者。
Improving Pain Classification using Spatio-Temporal Deep Learning Approaches with Facial Expressions
- 结合卷积与时序网络分析面部动态变化
- 在PEMF数据集上实现高精度二分类
- 首次在该数据集应用时空模型，适合医疗辅助场景
疼痛管理与严重程度评估对有效治疗至关重要，但传统自评方法主观性强，且不适用于非语言人群（如语言能力受限者）。为解决此问题，本研究探索基于面部表情的自动化疼痛检测。通过深度学习技术分析来自疼痛情绪面部数据库（Pain Emotion Faces Database, PEMF）的面部图像，提出两种新方法：(1) 融合ConvNeXt与长短期记忆（LSTM）块的混合模型，用于视频帧分析并预测疼痛存在；(2) 结合时空图卷积网络（STGCN）与LSTM的模型，处理面部关键点序列进行疼痛识别。本研究首次将PEMF数据集应用于二值疼痛分类任务，并通过大量实验验证了模型有效性。结果表明，融合空间与时间特征可显著提升疼痛检测性能，为客观疼痛评估提供了有前景的新路径。
原文摘要 · Abstract (English)
Pain management and severity detection are crucial for effective treatment, yet traditional self-reporting methods are subjective and may be unsuitable for non-verbal individuals (people with limited speaking skills). To address this limitation, we explore automated pain detection using facial expressions. Our study leverages deep learning techniques to improve pain assessment by analyzing facial images from the Pain Emotion Faces Database (PEMF). We propose two novel approaches1: (1) a hybrid ConvNeXt model combined with Long Short-Term Memory (LSTM) blocks to analyze video frames and predict pain presence, and (2) a Spatio-Temporal Graph Convolution Network (STGCN) integrated with LSTM to process landmarks from facial images for pain detection. Our work represents the first use of the PEMF dataset for binary pain classification and demonstrates the effectiveness of these models through extensive experimentation. The results highlight the potential of combining spatial and temporal features for enhanced pain detection, offering a promising advancement in objective pain assessment methodologies.
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